Ron Mitchell

Short summary AI “agents” — AI tools that act on your systems, follow multi-step tasks, and remember context — have moved from demos to real work. Over the last year vendors have added easier connectors (CRMs, calendars, email), persistent memory, and safer orchestration tools. The result: teams can automate prospecting workflows, routine customer follow-ups, and […]

Summary — the story in plain language – In the past year we’ve moved from catchy demos to real, enterprise-ready AI agents: tools that can take multi-step actions (send emails, pull data, update CRMs, generate reports) without a human doing every click. – Big vendors and new startups are baking agents into low-code platforms and

Quick summary AI agents — autonomous, task-focused AI programs that can act on your behalf — have moved beyond experiments. More companies are using them to triage leads, run sales outreach sequences, auto-update CRMs, and generate routine business reports. The result: faster workflows, fewer manual errors, and measurable time savings across revenue and operations teams.

Story summary Autonomous AI agents — AI systems that can plan, act, and use tools on their own — have moved beyond lab demos into real business pilots. Improvements in large language models, tool integration (APIs, RPA, BI tools), and safer guardrails mean companies are starting to deploy agents for tasks like lead research, customer

Quick story AI agents and custom LLMs (think company-specific chatbots that can act, fetch data, and run tasks) moved from lab experiments to practical business tools in 2024. Major platforms now make it straightforward to connect LLMs to company data, calendars, CRMs, and reporting systems — so an AI agent can draft outreach, pull sales

Quick summary In the last year we’ve moved past “wow” demos to real deployments of autonomous AI agents — small, task-focused bots that can read your company data, take actions (like sending emails or updating a CRM), and generate reports. Vendors and startups are packaging agent orchestration, retrieval-augmented generation (RAG), and secure connectors so these

Quick summary – Over the past year major CRM and productivity vendors have embedded AI agents into sales and operations: automated follow-ups, real-time pipeline forecasting, meeting summaries, and AI-powered reporting that pulls from your CRM, email, and docs. – These agents aren’t just chatbots. They can run multi-step tasks (research leads, draft outreach, log activity),

Quick summary – The European Union has moved its landmark AI regulation — the “AI Act” — from negotiation to implementation. The law takes a risk-based approach: some AI uses are banned, many are labeled “high-risk” and must meet strict requirements, and transparency and logging are mandatory for a wide range of systems. – This

Quick summary – Over the past year, AI agents — autonomous software that can complete multi-step tasks — have moved from flashy demos into real, enterprise-ready tools. Major cloud and SaaS vendors rolled out agent frameworks and connectors that let these agents access calendars, CRMs, document stores, and reporting systems. – That shift matters because

Quick summary AI agents — autonomous, task-focused assistants powered by large language models and integrations — have moved from experiments to real business use. Vendors and startups are embedding agents into CRMs, BI tools, and customer platforms so agents can do things like qualify leads, update records, draft outreach, and generate routine reports without constant